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Detection and classification of anomalous events in water quality datasets within a smart city-smart bay project

Zhang, Dian orcid logoORCID: 0000-0001-5659-5865, Sullivan, Timothy orcid logoORCID: 0000-0002-1093-0602, Briciu Burghina, Ciprian Constantin orcid logoORCID: 0000-0001-8682-9116, Murphy, Kevin orcid logoORCID: 0000-0003-2446-4064, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Regan, Fiona orcid logoORCID: 0000-0002-8273-9970 (2014) Detection and classification of anomalous events in water quality datasets within a smart city-smart bay project. International Journal on Advances in Intelligent Systems, 7 (1&2). pp. 167-178. ISSN 1942-2679

Abstract
Continual measurement is key to understanding sudden and gradual changes in chemical and biological quality of water, and for taking reactive remedial action in the case of contamination. Monitoring of water bodies will increase in future within Europe to comply with legislative requirements such as the Water Framework Directive and globally owing to pressure from climate change. Establishing high quality long-term monitoring programs is regarded as essential if the implementation of pertinent legislation is to be successful. However, conventional discrete sampling programs and laboratory-based analysis techniques can be costly, and are unlikely to provide timely and reliable estimates of true ranges of deterministic physicochemical variability in a water body with marked temporal or spatial variability. Only continual or near continual measurements have the capacity to detect ephemeral or sporadic events, thus providing the potential for on-line event detection and classification. The aim of this work is to demonstrate the potential role of continuous data acquisition in decision support as part of a smart city project. In this work, a multi-modal smart sensor network system framework for large scale estuarine and marine water quality monitoring is proposed. The application of a number of evolving techniques that allow automated detection and clustering of events from data generated by in situ sensors within environmental time series datasets is described. We provide examples of how change in the range of variables such as turbidity and salinity might be detected and clustered to provide the end user with greater ability to detect the onset of environmentally significant events. Finally, we discuss the acquisition of data from in situ water quality sensors and its utility within the framework a smart city-smart bay integrated project.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Continuous water monitoring; Estuary, Marine; Decision support; Turbidity; Salinity; Anomaly detection; Robust online clustering; Pixel-based adaptive segmentation
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Marine and Environmental Sensing Technology Hub (MESTECH)
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Publisher:IARIA
Official URL:http://www.iariajournals.org/intelligent_systems/i...
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:20053
Deposited On:22 Jul 2014 09:38 by Mr. Dian Zhang . Last Modified 04 May 2022 10:10
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